Overview

Dataset statistics

Number of variables39
Number of observations103063
Missing cells78812
Missing cells (%)2.0%
Duplicate rows3771
Duplicate rows (%)3.7%
Total size in memory30.7 MiB
Average record size in memory312.0 B

Variable types

Categorical18
Numeric21

Alerts

cd_value has constant value "Not Applicable"Constant
index_right has constant value "0"Constant
emsr_id has constant value "EMSR648"Constant
Dataset has 3771 (3.7%) duplicate rowsDuplicates
name has a high cardinality: 5082 distinct valuesHigh cardinality
info has a high cardinality: 52 distinct valuesHigh cardinality
geometry has a high cardinality: 98670 distinct valuesHigh cardinality
or_src_id is highly overall correlated with building_perm and 6 other fieldsHigh correlation
area_id is highly overall correlated with population and 14 other fieldsHigh correlation
population is highly overall correlated with area_id and 14 other fieldsHigh correlation
income is highly overall correlated with building_perm and 9 other fieldsHigh correlation
total_sales is highly overall correlated with area_id and 17 other fieldsHigh correlation
second_sales is highly overall correlated with area_id and 17 other fieldsHigh correlation
elec_cons is highly overall correlated with population and 8 other fieldsHigh correlation
building_perm is highly overall correlated with or_src_id and 14 other fieldsHigh correlation
land_permited is highly overall correlated with or_src_id and 12 other fieldsHigh correlation
agricultural is highly overall correlated with fertility and 2 other fieldsHigh correlation
life_time is highly overall correlated with population and 13 other fieldsHigh correlation
hb_per100000 is highly overall correlated with or_src_id and 10 other fieldsHigh correlation
fertility is highly overall correlated with population and 12 other fieldsHigh correlation
hh_size is highly overall correlated with population and 10 other fieldsHigh correlation
longitude is highly overall correlated with population and 14 other fieldsHigh correlation
latitude is highly overall correlated with life_time and 7 other fieldsHigh correlation
nearest_water_source_distance is highly overall correlated with det_method and 1 other fieldsHigh correlation
nearest_camping_distance is highly overall correlated with locality and 3 other fieldsHigh correlation
nearest_earthquake_distance is highly overall correlated with population and 8 other fieldsHigh correlation
nearest_fault_distance is highly overall correlated with population and 10 other fieldsHigh correlation
elev is highly overall correlated with area_id and 12 other fieldsHigh correlation
obj_type is highly overall correlated with or_src_id and 2 other fieldsHigh correlation
info is highly overall correlated with or_src_id and 2 other fieldsHigh correlation
damage_gra is highly overall correlated with det_methodHigh correlation
det_method is highly overall correlated with area_id and 4 other fieldsHigh correlation
notation is highly overall correlated with or_src_id and 5 other fieldsHigh correlation
dmg_src_id is highly overall correlated with area_id and 3 other fieldsHigh correlation
real is highly overall correlated with area_id and 15 other fieldsHigh correlation
glide_no is highly overall correlated with area_id and 10 other fieldsHigh correlation
locality is highly overall correlated with or_src_id and 28 other fieldsHigh correlation
map_type is highly overall correlated with area_id and 19 other fieldsHigh correlation
water_access is highly overall correlated with area_id and 19 other fieldsHigh correlation
labour_fource is highly overall correlated with area_id and 21 other fieldsHigh correlation
unemployment is highly overall correlated with area_id and 21 other fieldsHigh correlation
obj_type is highly imbalanced (52.4%)Imbalance
name is highly imbalanced (86.5%)Imbalance
info is highly imbalanced (72.0%)Imbalance
damage_gra is highly imbalanced (80.5%)Imbalance
det_method is highly imbalanced (96.0%)Imbalance
real is highly imbalanced (61.1%)Imbalance
glide_no is highly imbalanced (60.6%)Imbalance
real has 78802 (76.5%) missing valuesMissing
geometry is uniformly distributedUniform

Reproduction

Analysis started2023-08-04 20:05:30.395038
Analysis finished2023-08-04 20:06:51.529678
Duration1 minute and 21.13 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

obj_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
211-Highways, Streets and Roads
49090 
11-Residential Buildings
39979 
995-Unclassified
10555 
12-Non-residential Buildings
 
2494
24-Other Civil Engineering Works
 
558
Other values (5)
 
387

Length

Max length51
Median length49
Mean length26.623725
Min length12

Characters and Unicode

Total characters2743921
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11-Residential Buildings
2nd row11-Residential Buildings
3rd row12-Non-residential Buildings
4th row12-Non-residential Buildings
5th row11-Residential Buildings

Common Values

ValueCountFrequency (%)
211-Highways, Streets and Roads 49090
47.6%
11-Residential Buildings 39979
38.8%
995-Unclassified 10555
 
10.2%
12-Non-residential Buildings 2494
 
2.4%
24-Other Civil Engineering Works 558
 
0.5%
212-Railways 334
 
0.3%
22-Pipelines, Communication and Electricity Lines 19
 
< 0.1%
214-Bridges, Elevated Highways, Tunnels and Subways 16
 
< 0.1%
213-Airfield 15
 
< 0.1%
23-Complex Constructions on Industrial Sites 3
 
< 0.1%

Length

2023-08-04T22:06:51.608783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:51.748975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
and 49125
16.7%
211-highways 49090
16.7%
roads 49090
16.7%
streets 49090
16.7%
buildings 42473
14.4%
11-residential 39979
13.6%
995-unclassified 10555
 
3.6%
12-non-residential 2494
 
0.8%
24-other 558
 
0.2%
civil 558
 
0.2%
Other values (18) 1636
 
0.6%

Most occurring characters

ValueCountFrequency (%)
s 254332
 
9.3%
i 242862
 
8.9%
a 201071
 
7.3%
e 195516
 
7.1%
d 193766
 
7.1%
191585
 
7.0%
1 180997
 
6.6%
n 148914
 
5.4%
t 141296
 
5.1%
- 105557
 
3.8%
Other values (37) 888025
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1885982
68.7%
Decimal Number 266136
 
9.7%
Uppercase Letter 245520
 
8.9%
Space Separator 191585
 
7.0%
Dash Punctuation 105557
 
3.8%
Other Punctuation 49141
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 254332
13.5%
i 242862
12.9%
a 201071
10.7%
e 195516
10.4%
d 193766
10.3%
n 148914
7.9%
t 141296
7.5%
l 96484
 
5.1%
g 92711
 
4.9%
r 53314
 
2.8%
Other values (13) 265716
14.1%
Uppercase Letter
ValueCountFrequency (%)
R 89403
36.4%
S 49109
20.0%
H 49106
20.0%
B 42489
17.3%
U 10555
 
4.3%
N 2494
 
1.0%
E 593
 
0.2%
C 583
 
0.2%
W 558
 
0.2%
O 558
 
0.2%
Other values (5) 72
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 180997
68.0%
2 52882
 
19.9%
9 21110
 
7.9%
5 10555
 
4.0%
4 574
 
0.2%
3 18
 
< 0.1%
Space Separator
ValueCountFrequency (%)
191585
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 105557
100.0%
Other Punctuation
ValueCountFrequency (%)
, 49141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2131502
77.7%
Common 612419
 
22.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 254332
11.9%
i 242862
11.4%
a 201071
9.4%
e 195516
9.2%
d 193766
9.1%
n 148914
 
7.0%
t 141296
 
6.6%
l 96484
 
4.5%
g 92711
 
4.3%
R 89403
 
4.2%
Other values (28) 475147
22.3%
Common
ValueCountFrequency (%)
191585
31.3%
1 180997
29.6%
- 105557
17.2%
2 52882
 
8.6%
, 49141
 
8.0%
9 21110
 
3.4%
5 10555
 
1.7%
4 574
 
0.1%
3 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2743921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 254332
 
9.3%
i 242862
 
8.9%
a 201071
 
7.3%
e 195516
 
7.1%
d 193766
 
7.1%
191585
 
7.0%
1 180997
 
6.6%
n 148914
 
5.4%
t 141296
 
5.1%
- 105557
 
3.8%
Other values (37) 888025
32.4%

name
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct5082
Distinct (%)4.9%
Missing10
Missing (%)< 0.1%
Memory size805.3 KiB
Unknown
88874 
Battalgazi Konakları
 
3548
Antakya
 
2877
Atatürk Bulvarı
 
56
Osmaniye Sanayi Sitesi
 
56
Other values (5077)
 
7642

Length

Max length67
Median length7
Mean length8.0455494
Min length1

Characters and Unicode

Total characters829118
Distinct characters86
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3973 ?
Unique (%)3.9%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 88874
86.2%
Battalgazi Konakları 3548
 
3.4%
Antakya 2877
 
2.8%
Atatürk Bulvarı 56
 
0.1%
Osmaniye Sanayi Sitesi 56
 
0.1%
Fakıuşağı 47
 
< 0.1%
Gaziantep-Osmaniye yolu 45
 
< 0.1%
Malatya-Elazığ yolu 45
 
< 0.1%
Gaziantep Çevre Yolu 28
 
< 0.1%
Antakya-İskenderun yolu 26
 
< 0.1%
Other values (5072) 7451
 
7.2%

Length

2023-08-04T22:06:51.890194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown 88874
76.4%
sokak 3578
 
3.1%
battalgazi 3550
 
3.1%
konaklarä± 3548
 
3.0%
antakya 2883
 
2.5%
caddesi 944
 
0.8%
bulvarä± 474
 
0.4%
yolu 414
 
0.4%
cadde 317
 
0.3%
geã§it 200
 
0.2%
Other values (4704) 11573
 
9.9%

Most occurring characters

ValueCountFrequency (%)
n 275471
33.2%
k 104136
 
12.6%
o 97495
 
11.8%
U 88936
 
10.7%
w 88885
 
10.7%
a 34888
 
4.2%
13302
 
1.6%
t 12148
 
1.5%
l 10383
 
1.3%
i 7917
 
1.0%
Other values (76) 95557
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 667553
80.5%
Uppercase Letter 119927
 
14.5%
Decimal Number 15537
 
1.9%
Space Separator 13303
 
1.6%
Math Symbol 5280
 
0.6%
Other Punctuation 4522
 
0.5%
Control 1575
 
0.2%
Other Number 746
 
0.1%
Dash Punctuation 373
 
< 0.1%
Other Symbol 289
 
< 0.1%
Other values (3) 13
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 88936
74.2%
Ä 6105
 
5.1%
B 4447
 
3.7%
S 4163
 
3.5%
K 4112
 
3.4%
A 3891
 
3.2%
à 1665
 
1.4%
C 1536
 
1.3%
G 658
 
0.5%
Ã… 634
 
0.5%
Other values (18) 3780
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
n 275471
41.3%
k 104136
 
15.6%
o 97495
 
14.6%
w 88885
 
13.3%
a 34888
 
5.2%
t 12148
 
1.8%
l 10383
 
1.6%
i 7917
 
1.2%
r 6440
 
1.0%
e 4778
 
0.7%
Other values (15) 25012
 
3.7%
Decimal Number
ValueCountFrequency (%)
1 3083
19.8%
2 2541
16.4%
0 2187
14.1%
3 1282
8.3%
6 1278
8.2%
5 1272
8.2%
4 1109
 
7.1%
9 989
 
6.4%
7 965
 
6.2%
8 831
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 3940
87.1%
§ 380
 
8.4%
¶ 132
 
2.9%
/ 65
 
1.4%
& 3
 
0.1%
' 1
 
< 0.1%
, 1
 
< 0.1%
Control
ValueCountFrequency (%)
Ÿ 922
58.5%
‡ 289
 
18.3%
ž 248
 
15.7%
– 75
 
4.8%
œ 38
 
2.4%
‚ 3
 
0.2%
Space Separator
ValueCountFrequency (%)
13302
> 99.9%
  1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
° 288
99.7%
® 1
 
0.3%
Math Symbol
ValueCountFrequency (%)
± 5280
100.0%
Other Number
ValueCountFrequency (%)
¼ 746
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 373
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 787480
95.0%
Common 41638
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 275471
35.0%
k 104136
 
13.2%
o 97495
 
12.4%
U 88936
 
11.3%
w 88885
 
11.3%
a 34888
 
4.4%
t 12148
 
1.5%
l 10383
 
1.3%
i 7917
 
1.0%
r 6440
 
0.8%
Other values (43) 60781
 
7.7%
Common
ValueCountFrequency (%)
13302
31.9%
± 5280
 
12.7%
. 3940
 
9.5%
1 3083
 
7.4%
2 2541
 
6.1%
0 2187
 
5.3%
3 1282
 
3.1%
6 1278
 
3.1%
5 1272
 
3.1%
4 1109
 
2.7%
Other values (23) 6364
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 812310
98.0%
None 16808
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 275471
33.9%
k 104136
 
12.8%
o 97495
 
12.0%
U 88936
 
10.9%
w 88885
 
10.9%
a 34888
 
4.3%
13302
 
1.6%
t 12148
 
1.5%
l 10383
 
1.3%
i 7917
 
1.0%
Other values (59) 78749
 
9.7%
None
ValueCountFrequency (%)
Ä 6105
36.3%
± 5280
31.4%
à 1665
 
9.9%
Ÿ 922
 
5.5%
¼ 746
 
4.4%
Ã… 634
 
3.8%
§ 380
 
2.3%
‡ 289
 
1.7%
° 288
 
1.7%
ž 248
 
1.5%
Other values (7) 251
 
1.5%

info
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE 

Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
997-Not Applicable
51520 
21122-Local Road
43155 
21121-Secondary Road
 
2219
21124-Cart Track
 
1564
2111-Highways
 
1100
Other values (47)
 
3505

Length

Max length74
Median length18
Mean length17.781066
Min length3

Characters and Unicode

Total characters1832570
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row997-Not Applicable
2nd row997-Not Applicable
3rd row1251-Industrial buildings
4th row1251-Industrial buildings
5th row997-Not Applicable

Common Values

ValueCountFrequency (%)
997-Not Applicable 51520
50.0%
21122-Local Road 43155
41.9%
21121-Secondary Road 2219
 
2.2%
21124-Cart Track 1564
 
1.5%
2111-Highways 1100
 
1.1%
1272-Buildings used as places of worship and for religious activities 561
 
0.5%
1251-Industrial buildings 521
 
0.5%
21120-Primary Road 503
 
0.5%
2412-Other sport and recreation constructions 484
 
0.5%
1263-School, university and research buildings 472
 
0.5%
Other values (42) 964
 
0.9%

Length

2023-08-04T22:06:52.006655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
997-not 51520
24.2%
applicable 51520
24.2%
road 45877
21.5%
21122-local 43155
20.3%
21121-secondary 2219
 
1.0%
and 1719
 
0.8%
track 1564
 
0.7%
21124-cart 1564
 
0.7%
buildings 1288
 
0.6%
2111-highways 1100
 
0.5%
Other values (96) 11544
 
5.4%

Most occurring characters

ValueCountFrequency (%)
a 154132
 
8.4%
l 151240
 
8.3%
o 148925
 
8.1%
2 143688
 
7.8%
110007
 
6.0%
p 104781
 
5.7%
1 104439
 
5.7%
- 103331
 
5.6%
9 103063
 
5.6%
c 102508
 
5.6%
Other values (42) 606456
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1008690
55.0%
Decimal Number 408048
22.3%
Uppercase Letter 201979
 
11.0%
Space Separator 110007
 
6.0%
Dash Punctuation 103331
 
5.6%
Other Punctuation 515
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 154132
15.3%
l 151240
15.0%
o 148925
14.8%
p 104781
10.4%
c 102508
10.2%
i 64125
6.4%
e 60572
 
6.0%
t 58911
 
5.8%
d 53389
 
5.3%
b 52842
 
5.2%
Other values (13) 57265
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 51534
25.5%
N 51525
25.5%
R 45914
22.7%
L 43474
21.5%
S 2769
 
1.4%
C 1610
 
0.8%
T 1584
 
0.8%
H 1176
 
0.6%
I 580
 
0.3%
B 576
 
0.3%
Other values (6) 1237
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 143688
35.2%
1 104439
25.6%
9 103063
25.3%
7 52173
 
12.8%
4 2267
 
0.6%
5 613
 
0.2%
3 607
 
0.1%
0 590
 
0.1%
6 561
 
0.1%
8 47
 
< 0.1%
Space Separator
ValueCountFrequency (%)
110007
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103331
100.0%
Other Punctuation
ValueCountFrequency (%)
, 515
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1210669
66.1%
Common 621901
33.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 154132
12.7%
l 151240
12.5%
o 148925
12.3%
p 104781
8.7%
c 102508
 
8.5%
i 64125
 
5.3%
e 60572
 
5.0%
t 58911
 
4.9%
d 53389
 
4.4%
b 52842
 
4.4%
Other values (29) 259244
21.4%
Common
ValueCountFrequency (%)
2 143688
23.1%
110007
17.7%
1 104439
16.8%
- 103331
16.6%
9 103063
16.6%
7 52173
 
8.4%
4 2267
 
0.4%
5 613
 
0.1%
3 607
 
0.1%
0 590
 
0.1%
Other values (3) 1123
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1832570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 154132
 
8.4%
l 151240
 
8.3%
o 148925
 
8.1%
2 143688
 
7.8%
110007
 
6.0%
p 104781
 
5.7%
1 104439
 
5.7%
- 103331
 
5.6%
9 103063
 
5.6%
c 102508
 
5.6%
Other values (42) 606456
33.1%

damage_gra
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
No visible damage
96633 
Damaged
 
2287
Possibly damaged
 
2098
Destroyed
 
1522
Not Analysed
 
523

Length

Max length17
Median length17
Mean length16.614226
Min length7

Characters and Unicode

Total characters1712312
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo visible damage
2nd rowNo visible damage
3rd rowNo visible damage
4th rowNo visible damage
5th rowNo visible damage

Common Values

ValueCountFrequency (%)
No visible damage 96633
93.8%
Damaged 2287
 
2.2%
Possibly damaged 2098
 
2.0%
Destroyed 1522
 
1.5%
Not Analysed 523
 
0.5%

Length

2023-08-04T22:06:52.104360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:52.196437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 96633
32.3%
visible 96633
32.3%
damage 96633
32.3%
damaged 4385
 
1.5%
possibly 2098
 
0.7%
destroyed 1522
 
0.5%
not 523
 
0.2%
analysed 523
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 202559
11.8%
e 201218
11.8%
195887
11.4%
i 195364
11.4%
d 105161
 
6.1%
s 102874
 
6.0%
m 101018
 
5.9%
g 101018
 
5.9%
o 100776
 
5.9%
l 99254
 
5.8%
Other values (10) 307183
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1412839
82.5%
Space Separator 195887
 
11.4%
Uppercase Letter 103586
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 202559
14.3%
e 201218
14.2%
i 195364
13.8%
d 105161
7.4%
s 102874
7.3%
m 101018
7.2%
g 101018
7.2%
o 100776
7.1%
l 99254
7.0%
b 98731
7.0%
Other values (5) 104866
7.4%
Uppercase Letter
ValueCountFrequency (%)
N 97156
93.8%
D 3809
 
3.7%
P 2098
 
2.0%
A 523
 
0.5%
Space Separator
ValueCountFrequency (%)
195887
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1516425
88.6%
Common 195887
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 202559
13.4%
e 201218
13.3%
i 195364
12.9%
d 105161
6.9%
s 102874
6.8%
m 101018
6.7%
g 101018
6.7%
o 100776
6.6%
l 99254
6.5%
b 98731
6.5%
Other values (9) 208452
13.7%
Common
ValueCountFrequency (%)
195887
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1712312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 202559
11.8%
e 201218
11.8%
195887
11.4%
i 195364
11.4%
d 105161
 
6.1%
s 102874
 
6.0%
m 101018
 
5.9%
g 101018
 
5.9%
o 100776
 
5.9%
l 99254
 
5.8%
Other values (10) 307183
17.9%

det_method
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
Photo-interpretation
102616 
Not Applicable
 
447

Length

Max length20
Median length20
Mean length19.973977
Min length14

Characters and Unicode

Total characters2058578
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoto-interpretation
2nd rowPhoto-interpretation
3rd rowPhoto-interpretation
4th rowPhoto-interpretation
5th rowPhoto-interpretation

Common Values

ValueCountFrequency (%)
Photo-interpretation 102616
99.6%
Not Applicable 447
 
0.4%

Length

2023-08-04T22:06:52.294474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:52.392752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
photo-interpretation 102616
99.1%
not 447
 
0.4%
applicable 447
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 410911
20.0%
o 308295
15.0%
i 205679
10.0%
e 205679
10.0%
r 205232
10.0%
n 205232
10.0%
p 103510
 
5.0%
a 103063
 
5.0%
P 102616
 
5.0%
h 102616
 
5.0%
Other values (7) 105745
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1852005
90.0%
Uppercase Letter 103510
 
5.0%
Dash Punctuation 102616
 
5.0%
Space Separator 447
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 410911
22.2%
o 308295
16.6%
i 205679
11.1%
e 205679
11.1%
r 205232
11.1%
n 205232
11.1%
p 103510
 
5.6%
a 103063
 
5.6%
h 102616
 
5.5%
l 894
 
< 0.1%
Other values (2) 894
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 102616
99.1%
N 447
 
0.4%
A 447
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 102616
100.0%
Space Separator
ValueCountFrequency (%)
447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1955515
95.0%
Common 103063
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 410911
21.0%
o 308295
15.8%
i 205679
10.5%
e 205679
10.5%
r 205232
10.5%
n 205232
10.5%
p 103510
 
5.3%
a 103063
 
5.3%
P 102616
 
5.2%
h 102616
 
5.2%
Other values (5) 2682
 
0.1%
Common
ValueCountFrequency (%)
- 102616
99.6%
447
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2058578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 410911
20.0%
o 308295
15.0%
i 205679
10.0%
e 205679
10.0%
r 205232
10.0%
n 205232
10.0%
p 103510
 
5.0%
a 103063
 
5.0%
P 102616
 
5.0%
h 102616
 
5.0%
Other values (7) 105745
 
5.1%

notation
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
Not Applicable
50035 
Building point
28767 
Building block
24261 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1442882
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuilding block
2nd rowBuilding block
3rd rowBuilding block
4th rowBuilding block
5th rowBuilding block

Common Values

ValueCountFrequency (%)
Not Applicable 50035
48.5%
Building point 28767
27.9%
Building block 24261
23.5%

Length

2023-08-04T22:06:52.661616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:52.757215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
building 53028
25.7%
not 50035
24.3%
applicable 50035
24.3%
point 28767
14.0%
block 24261
11.8%

Most occurring characters

ValueCountFrequency (%)
i 184858
12.8%
l 177359
12.3%
p 128837
 
8.9%
103063
 
7.1%
o 103063
 
7.1%
n 81795
 
5.7%
t 78802
 
5.5%
c 74296
 
5.1%
b 74296
 
5.1%
d 53028
 
3.7%
Other values (8) 383485
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1186721
82.2%
Uppercase Letter 153098
 
10.6%
Space Separator 103063
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 184858
15.6%
l 177359
14.9%
p 128837
10.9%
o 103063
8.7%
n 81795
6.9%
t 78802
6.6%
c 74296
6.3%
b 74296
6.3%
d 53028
 
4.5%
g 53028
 
4.5%
Other values (4) 177359
14.9%
Uppercase Letter
ValueCountFrequency (%)
B 53028
34.6%
N 50035
32.7%
A 50035
32.7%
Space Separator
ValueCountFrequency (%)
103063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1339819
92.9%
Common 103063
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 184858
13.8%
l 177359
13.2%
p 128837
 
9.6%
o 103063
 
7.7%
n 81795
 
6.1%
t 78802
 
5.9%
c 74296
 
5.5%
b 74296
 
5.5%
d 53028
 
4.0%
g 53028
 
4.0%
Other values (7) 330457
24.7%
Common
ValueCountFrequency (%)
103063
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1442882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 184858
12.8%
l 177359
12.3%
p 128837
 
8.9%
103063
 
7.1%
o 103063
 
7.1%
n 81795
 
5.7%
t 78802
 
5.5%
c 74296
 
5.1%
b 74296
 
5.1%
d 53028
 
3.7%
Other values (8) 383485
26.6%

or_src_id
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean604.26548
Minimum1
Maximum997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:52.832849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median994
Q3994
95-th percentile994
Maximum997
Range996
Interquartile range (IQR)993

Descriptive statistics

Standard deviation484.83179
Coefficient of variation (CV)0.80234898
Kurtosis-1.8063043
Mean604.26548
Median Absolute Deviation (MAD)0
Skewness-0.44014701
Sum62277413
Variance235061.86
MonotonicityNot monotonic
2023-08-04T22:06:52.908392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
994 62606
60.7%
1 36192
35.1%
2 3269
 
3.2%
3 658
 
0.6%
4 337
 
0.3%
997 1
 
< 0.1%
ValueCountFrequency (%)
1 36192
35.1%
2 3269
 
3.2%
3 658
 
0.6%
4 337
 
0.3%
994 62606
60.7%
997 1
 
< 0.1%
ValueCountFrequency (%)
997 1
 
< 0.1%
994 62606
60.7%
4 337
 
0.3%
3 658
 
0.6%
2 3269
 
3.2%
1 36192
35.1%

dmg_src_id
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
3
62004 
2
39435 
997
 
1094
4
 
530

Length

Max length3
Median length1
Mean length1.0212297
Min length1

Characters and Unicode

Total characters105251
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 62004
60.2%
2 39435
38.3%
997 1094
 
1.1%
4 530
 
0.5%

Length

2023-08-04T22:06:53.007830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:53.110162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 62004
60.2%
2 39435
38.3%
997 1094
 
1.1%
4 530
 
0.5%

Most occurring characters

ValueCountFrequency (%)
3 62004
58.9%
2 39435
37.5%
9 2188
 
2.1%
7 1094
 
1.0%
4 530
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 62004
58.9%
2 39435
37.5%
9 2188
 
2.1%
7 1094
 
1.0%
4 530
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 105251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 62004
58.9%
2 39435
37.5%
9 2188
 
2.1%
7 1094
 
1.0%
4 530
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 62004
58.9%
2 39435
37.5%
9 2188
 
2.1%
7 1094
 
1.0%
4 530
 
0.5%

cd_value
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
Not Applicable
103063 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1442882
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable 103063
100.0%

Length

2023-08-04T22:06:53.187386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:53.272526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
not 103063
50.0%
applicable 103063
50.0%

Most occurring characters

ValueCountFrequency (%)
p 206126
14.3%
l 206126
14.3%
N 103063
7.1%
o 103063
7.1%
t 103063
7.1%
103063
7.1%
A 103063
7.1%
i 103063
7.1%
c 103063
7.1%
a 103063
7.1%
Other values (2) 206126
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1133693
78.6%
Uppercase Letter 206126
 
14.3%
Space Separator 103063
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 206126
18.2%
l 206126
18.2%
o 103063
9.1%
t 103063
9.1%
i 103063
9.1%
c 103063
9.1%
a 103063
9.1%
b 103063
9.1%
e 103063
9.1%
Uppercase Letter
ValueCountFrequency (%)
N 103063
50.0%
A 103063
50.0%
Space Separator
ValueCountFrequency (%)
103063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1339819
92.9%
Common 103063
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 206126
15.4%
l 206126
15.4%
N 103063
7.7%
o 103063
7.7%
t 103063
7.7%
A 103063
7.7%
i 103063
7.7%
c 103063
7.7%
a 103063
7.7%
b 103063
7.7%
Common
ValueCountFrequency (%)
103063
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1442882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 206126
14.3%
l 206126
14.3%
N 103063
7.1%
o 103063
7.1%
t 103063
7.1%
103063
7.1%
A 103063
7.1%
i 103063
7.1%
c 103063
7.1%
a 103063
7.1%
Other values (2) 206126
14.3%

real
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing78802
Missing (%)76.5%
Memory size805.3 KiB
Not Applicable
22410 
None
 
1851

Length

Max length14
Median length14
Mean length13.237047
Min length4

Characters and Unicode

Total characters321144
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable 22410
 
21.7%
None 1851
 
1.8%
(Missing) 78802
76.5%

Length

2023-08-04T22:06:53.343471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:53.432872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
not 22410
48.0%
applicable 22410
48.0%
none 1851
 
4.0%

Most occurring characters

ValueCountFrequency (%)
p 44820
14.0%
l 44820
14.0%
N 24261
7.6%
o 24261
7.6%
e 24261
7.6%
t 22410
7.0%
22410
7.0%
A 22410
7.0%
i 22410
7.0%
c 22410
7.0%
Other values (3) 46671
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 252063
78.5%
Uppercase Letter 46671
 
14.5%
Space Separator 22410
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 44820
17.8%
l 44820
17.8%
o 24261
9.6%
e 24261
9.6%
t 22410
8.9%
i 22410
8.9%
c 22410
8.9%
a 22410
8.9%
b 22410
8.9%
n 1851
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
N 24261
52.0%
A 22410
48.0%
Space Separator
ValueCountFrequency (%)
22410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 298734
93.0%
Common 22410
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 44820
15.0%
l 44820
15.0%
N 24261
8.1%
o 24261
8.1%
e 24261
8.1%
t 22410
7.5%
A 22410
7.5%
i 22410
7.5%
c 22410
7.5%
a 22410
7.5%
Other values (2) 24261
8.1%
Common
ValueCountFrequency (%)
22410
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 44820
14.0%
l 44820
14.0%
N 24261
7.6%
o 24261
7.6%
e 24261
7.6%
t 22410
7.0%
22410
7.0%
A 22410
7.0%
i 22410
7.0%
c 22410
7.0%
Other values (3) 46671
14.5%

index_right
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
0
103063 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103063
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 103063
100.0%

Length

2023-08-04T22:06:53.506076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:53.596825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 103063
100.0%

Most occurring characters

ValueCountFrequency (%)
0 103063
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103063
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103063
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103063
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103063
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103063
100.0%

emsr_id
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
EMSR648
103063 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters721441
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMSR648
2nd rowEMSR648
3rd rowEMSR648
4th rowEMSR648
5th rowEMSR648

Common Values

ValueCountFrequency (%)
EMSR648 103063
100.0%

Length

2023-08-04T22:06:53.665413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:53.747803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
emsr648 103063
100.0%

Most occurring characters

ValueCountFrequency (%)
E 103063
14.3%
M 103063
14.3%
S 103063
14.3%
R 103063
14.3%
6 103063
14.3%
4 103063
14.3%
8 103063
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 412252
57.1%
Decimal Number 309189
42.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 103063
25.0%
M 103063
25.0%
S 103063
25.0%
R 103063
25.0%
Decimal Number
ValueCountFrequency (%)
6 103063
33.3%
4 103063
33.3%
8 103063
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 412252
57.1%
Common 309189
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 103063
25.0%
M 103063
25.0%
S 103063
25.0%
R 103063
25.0%
Common
ValueCountFrequency (%)
6 103063
33.3%
4 103063
33.3%
8 103063
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 721441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 103063
14.3%
M 103063
14.3%
S 103063
14.3%
R 103063
14.3%
6 103063
14.3%
4 103063
14.3%
8 103063
14.3%

glide_no
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
2023-000015
95068 
Not Applicable
 
7995

Length

Max length14
Median length11
Mean length11.232722
Min length11

Characters and Unicode

Total characters1157678
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-000015
2nd row2023-000015
3rd row2023-000015
4th row2023-000015
5th row2023-000015

Common Values

ValueCountFrequency (%)
2023-000015 95068
92.2%
Not Applicable 7995
 
7.8%

Length

2023-08-04T22:06:53.815532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:53.905511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023-000015 95068
85.6%
not 7995
 
7.2%
applicable 7995
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 475340
41.1%
2 190136
 
16.4%
3 95068
 
8.2%
- 95068
 
8.2%
1 95068
 
8.2%
5 95068
 
8.2%
l 15990
 
1.4%
p 15990
 
1.4%
b 7995
 
0.7%
a 7995
 
0.7%
Other values (8) 63960
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 950680
82.1%
Dash Punctuation 95068
 
8.2%
Lowercase Letter 87945
 
7.6%
Uppercase Letter 15990
 
1.4%
Space Separator 7995
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 15990
18.2%
p 15990
18.2%
b 7995
9.1%
a 7995
9.1%
c 7995
9.1%
i 7995
9.1%
t 7995
9.1%
o 7995
9.1%
e 7995
9.1%
Decimal Number
ValueCountFrequency (%)
0 475340
50.0%
2 190136
 
20.0%
3 95068
 
10.0%
1 95068
 
10.0%
5 95068
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
A 7995
50.0%
N 7995
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 95068
100.0%
Space Separator
ValueCountFrequency (%)
7995
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1053743
91.0%
Latin 103935
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 15990
15.4%
p 15990
15.4%
b 7995
7.7%
a 7995
7.7%
c 7995
7.7%
i 7995
7.7%
A 7995
7.7%
t 7995
7.7%
o 7995
7.7%
N 7995
7.7%
Common
ValueCountFrequency (%)
0 475340
45.1%
2 190136
 
18.0%
3 95068
 
9.0%
- 95068
 
9.0%
1 95068
 
9.0%
5 95068
 
9.0%
7995
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1157678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 475340
41.1%
2 190136
 
16.4%
3 95068
 
8.2%
- 95068
 
8.2%
1 95068
 
8.2%
5 95068
 
8.2%
l 15990
 
1.4%
p 15990
 
1.4%
b 7995
 
0.7%
a 7995
 
0.7%
Other values (8) 63960
 
5.5%

area_id
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4783288
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:53.973700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q311
95-th percentile17
Maximum20
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.2753061
Coefficient of variation (CV)0.81430045
Kurtosis-0.4033683
Mean6.4783288
Median Absolute Deviation (MAD)4
Skewness0.8180777
Sum667676
Variance27.828854
MonotonicityNot monotonic
2023-08-04T22:06:54.057137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 24025
23.3%
4 12584
12.2%
7 11980
11.6%
2 9803
9.5%
5 8269
 
8.0%
11 8196
 
8.0%
14 7995
 
7.8%
6 5290
 
5.1%
15 3632
 
3.5%
10 2545
 
2.5%
Other values (9) 8744
 
8.5%
ValueCountFrequency (%)
1 24025
23.3%
2 9803
9.5%
3 890
 
0.9%
4 12584
12.2%
5 8269
 
8.0%
6 5290
 
5.1%
7 11980
11.6%
8 509
 
0.5%
10 2545
 
2.5%
11 8196
 
8.0%
ValueCountFrequency (%)
20 1688
 
1.6%
19 712
 
0.7%
18 2498
 
2.4%
17 485
 
0.5%
16 861
 
0.8%
15 3632
3.5%
14 7995
7.8%
13 521
 
0.5%
12 580
 
0.6%
11 8196
8.0%

locality
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
Gaziantep
24025 
Kahramanmaras
12584 
Sanliurfa
11980 
ADIYAMAN
9803 
Malatya
8269 
Other values (14)
36402 

Length

Max length13
Median length10
Mean length8.5336154
Min length5

Characters and Unicode

Total characters879500
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADIYAMAN
2nd rowADIYAMAN
3rd rowADIYAMAN
4th rowADIYAMAN
5th rowADIYAMAN

Common Values

ValueCountFrequency (%)
Gaziantep 24025
23.3%
Kahramanmaras 12584
12.2%
Sanliurfa 11980
11.6%
ADIYAMAN 9803
9.5%
Malatya 8269
 
8.0%
Antakya 8196
 
8.0%
Duzici 7995
 
7.8%
Osmaniye 5290
 
5.1%
Bahce 3632
 
3.5%
Islahiye 2545
 
2.5%
Other values (9) 8744
 
8.5%

Length

2023-08-04T22:06:54.158205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gaziantep 24025
23.3%
kahramanmaras 12584
12.2%
sanliurfa 11980
11.6%
adiyaman 9803
9.5%
malatya 8269
 
8.0%
antakya 8196
 
8.0%
duzici 7995
 
7.8%
osmaniye 5290
 
5.1%
bahce 3632
 
3.5%
islahiye 2545
 
2.5%
Other values (9) 8744
 
8.5%

Most occurring characters

ValueCountFrequency (%)
a 195962
22.3%
i 70897
 
8.1%
n 66973
 
7.6%
r 43861
 
5.0%
t 42178
 
4.8%
A 38317
 
4.4%
e 36072
 
4.1%
z 32529
 
3.7%
m 31038
 
3.5%
l 26068
 
3.0%
Other values (27) 295605
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 706656
80.3%
Uppercase Letter 172264
 
19.6%
Control 580
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 195962
27.7%
i 70897
 
10.0%
n 66973
 
9.5%
r 43861
 
6.2%
t 42178
 
6.0%
e 36072
 
5.1%
z 32529
 
4.6%
m 31038
 
4.4%
l 26068
 
3.7%
y 25190
 
3.6%
Other values (11) 135888
19.2%
Uppercase Letter
ValueCountFrequency (%)
A 38317
22.2%
G 24546
14.2%
D 18688
10.8%
M 18072
10.5%
K 15082
 
8.8%
I 12348
 
7.2%
S 11980
 
7.0%
N 10664
 
6.2%
Y 9803
 
5.7%
O 5290
 
3.1%
Other values (5) 7474
 
4.3%
Control
ValueCountFrequency (%)
Ÿ 580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 878920
99.9%
Common 580
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 195962
22.3%
i 70897
 
8.1%
n 66973
 
7.6%
r 43861
 
5.0%
t 42178
 
4.8%
A 38317
 
4.4%
e 36072
 
4.1%
z 32529
 
3.7%
m 31038
 
3.5%
l 26068
 
3.0%
Other values (26) 295025
33.6%
Common
ValueCountFrequency (%)
Ÿ 580
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 878340
99.9%
None 1160
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 195962
22.3%
i 70897
 
8.1%
n 66973
 
7.6%
r 43861
 
5.0%
t 42178
 
4.8%
A 38317
 
4.4%
e 36072
 
4.1%
z 32529
 
3.7%
m 31038
 
3.5%
l 26068
 
3.0%
Other values (25) 294445
33.5%
None
ValueCountFrequency (%)
Ä 580
50.0%
Ÿ 580
50.0%

map_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
Grading-Monit01
79119 
Grading
23944 

Length

Max length15
Median length15
Mean length13.141409
Min length7

Characters and Unicode

Total characters1354393
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrading-Monit01
2nd rowGrading-Monit01
3rd rowGrading-Monit01
4th rowGrading-Monit01
5th rowGrading-Monit01

Common Values

ValueCountFrequency (%)
Grading-Monit01 79119
76.8%
Grading 23944
 
23.2%

Length

2023-08-04T22:06:54.257249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:54.348675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
grading-monit01 79119
76.8%
grading 23944
 
23.2%

Most occurring characters

ValueCountFrequency (%)
i 182182
13.5%
n 182182
13.5%
G 103063
7.6%
r 103063
7.6%
a 103063
7.6%
d 103063
7.6%
g 103063
7.6%
- 79119
 
5.8%
M 79119
 
5.8%
o 79119
 
5.8%
Other values (3) 237357
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 934854
69.0%
Uppercase Letter 182182
 
13.5%
Decimal Number 158238
 
11.7%
Dash Punctuation 79119
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 182182
19.5%
n 182182
19.5%
r 103063
11.0%
a 103063
11.0%
d 103063
11.0%
g 103063
11.0%
o 79119
8.5%
t 79119
8.5%
Uppercase Letter
ValueCountFrequency (%)
G 103063
56.6%
M 79119
43.4%
Decimal Number
ValueCountFrequency (%)
0 79119
50.0%
1 79119
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 79119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1117036
82.5%
Common 237357
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 182182
16.3%
n 182182
16.3%
G 103063
9.2%
r 103063
9.2%
a 103063
9.2%
d 103063
9.2%
g 103063
9.2%
M 79119
7.1%
o 79119
7.1%
t 79119
7.1%
Common
ValueCountFrequency (%)
- 79119
33.3%
0 79119
33.3%
1 79119
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1354393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 182182
13.5%
n 182182
13.5%
G 103063
7.6%
r 103063
7.6%
a 103063
7.6%
d 103063
7.6%
g 103063
7.6%
- 79119
 
5.8%
M 79119
 
5.8%
o 79119
 
5.8%
Other values (3) 237357
17.5%

population
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1030681.5
Minimum22904
Maximum2170110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:54.421754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum22904
5-th percentile67650
Q1285430
median806936
Q32154051
95-th percentile2170110
Maximum2170110
Range2147206
Interquartile range (IQR)1868621

Descriptive statistics

Standard deviation872435.67
Coefficient of variation (CV)0.84646482
Kurtosis-1.6177654
Mean1030681.5
Median Absolute Deviation (MAD)721488
Skewness0.36725089
Sum1.0622513 × 1011
Variance7.6114399 × 1011
MonotonicityNot monotonic
2023-08-04T22:06:54.503443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2154051 24025
23.3%
806936 12584
12.2%
2170110 11980
11.6%
316140 10515
10.2%
853658 8269
 
8.0%
399045 8196
 
8.0%
85448 7995
 
7.8%
285430 5290
 
5.1%
22904 3632
 
3.5%
67650 2545
 
2.5%
Other values (7) 8032
 
7.8%
ValueCountFrequency (%)
22904 3632
3.5%
41322 861
 
0.8%
50326 521
 
0.5%
67650 2545
 
2.5%
70173 509
 
0.5%
77180 2268
 
2.2%
78976 485
 
0.5%
85448 7995
7.8%
121028 2498
 
2.4%
285430 5290
5.1%
ValueCountFrequency (%)
2170110 11980
11.6%
2154051 24025
23.3%
1804880 890
 
0.9%
853658 8269
 
8.0%
806936 12584
12.2%
399045 8196
 
8.0%
316140 10515
10.2%
285430 5290
 
5.1%
121028 2498
 
2.4%
85448 7995
 
7.8%

income
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6185.7078
Minimum3012
Maximum7819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:54.576543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3012
5-th percentile3012
Q15997
median6256
Q37819
95-th percentile7819
Maximum7819
Range4807
Interquartile range (IQR)1822

Descriptive statistics

Standard deviation1627.1628
Coefficient of variation (CV)0.263052
Kurtosis-0.64001779
Mean6185.7078
Median Absolute Deviation (MAD)1563
Skewness-0.75680155
Sum6.3751761 × 108
Variance2647658.8
MonotonicityNot monotonic
2023-08-04T22:06:54.655721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7819 35700
34.6%
6256 16917
16.4%
5997 15978
15.5%
3012 11980
 
11.6%
4092 10904
 
10.6%
6785 10694
 
10.4%
3893 890
 
0.9%
ValueCountFrequency (%)
3012 11980
 
11.6%
3893 890
 
0.9%
4092 10904
 
10.6%
5997 15978
15.5%
6256 16917
16.4%
6785 10694
 
10.4%
7819 35700
34.6%
ValueCountFrequency (%)
7819 35700
34.6%
6785 10694
 
10.4%
6256 16917
16.4%
5997 15978
15.5%
4092 10904
 
10.6%
3893 890
 
0.9%
3012 11980
 
11.6%

total_sales
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108321.2
Minimum1588
Maximum248121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:54.739708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1588
5-th percentile4498
Q132782
median87599
Q3177626
95-th percentile248121
Maximum248121
Range246533
Interquartile range (IQR)144844

Descriptive statistics

Standard deviation92216.014
Coefficient of variation (CV)0.85132009
Kurtosis-1.3380266
Mean108321.2
Median Absolute Deviation (MAD)81396
Skewness0.47464995
Sum1.1163908 × 1010
Variance8.5037933 × 109
MonotonicityNot monotonic
2023-08-04T22:06:54.834709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
248121 24025
23.3%
87599 12584
12.2%
177626 11980
11.6%
40087 9803
9.5%
96737 8269
 
8.0%
39858 8196
 
8.0%
6203 7995
 
7.8%
32782 5290
 
5.1%
1588 3632
 
3.5%
4498 2545
 
2.5%
Other values (9) 8744
 
8.5%
ValueCountFrequency (%)
1588 3632
3.5%
1938 485
 
0.5%
3019 861
 
0.8%
4498 2545
 
2.5%
5747 509
 
0.5%
6203 7995
7.8%
6455 521
 
0.5%
8762 712
 
0.7%
9675 580
 
0.6%
12466 2498
 
2.4%
ValueCountFrequency (%)
248121 24025
23.3%
177626 11980
11.6%
145244 890
 
0.9%
96737 8269
 
8.0%
87599 12584
12.2%
40087 9803
9.5%
39858 8196
 
8.0%
32782 5290
 
5.1%
18139 1688
 
1.6%
12466 2498
 
2.4%

second_sales
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60710.374
Minimum536
Maximum141434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:54.917008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum536
5-th percentile1892
Q119933
median42683
Q399010
95-th percentile141434
Maximum141434
Range140898
Interquartile range (IQR)79077

Descriptive statistics

Standard deviation52804.063
Coefficient of variation (CV)0.86977002
Kurtosis-1.317019
Mean60710.374
Median Absolute Deviation (MAD)40394
Skewness0.50202779
Sum6.2569932 × 109
Variance2.7882691 × 109
MonotonicityNot monotonic
2023-08-04T22:06:54.996817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
141434 24025
23.3%
42683 12584
12.2%
99010 11980
11.6%
20574 9803
9.5%
58495 8269
 
8.0%
24992 8196
 
8.0%
2289 7995
 
7.8%
19933 5290
 
5.1%
664 3632
 
3.5%
1892 2545
 
2.5%
Other values (9) 8744
 
8.5%
ValueCountFrequency (%)
536 485
 
0.5%
664 3632
3.5%
1060 861
 
0.8%
1892 2545
 
2.5%
1932 509
 
0.5%
2236 521
 
0.5%
2289 7995
7.8%
3300 712
 
0.7%
4098 580
 
0.6%
5881 2498
 
2.4%
ValueCountFrequency (%)
141434 24025
23.3%
99010 11980
11.6%
83985 890
 
0.9%
58495 8269
 
8.0%
42683 12584
12.2%
24992 8196
 
8.0%
20574 9803
9.5%
19933 5290
 
5.1%
10133 1688
 
1.6%
5881 2498
 
2.4%

water_access
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
0.95
45765 
1.0
35700 
0.98
10904 
0.99
10694 

Length

Max length4
Median length4
Mean length3.6536099
Min length3

Characters and Unicode

Total characters376552
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.98
2nd row0.98
3rd row0.98
4th row0.98
5th row0.98

Common Values

ValueCountFrequency (%)
0.95 45765
44.4%
1.0 35700
34.6%
0.98 10904
 
10.6%
0.99 10694
 
10.4%

Length

2023-08-04T22:06:55.098965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:55.199800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.95 45765
44.4%
1.0 35700
34.6%
0.98 10904
 
10.6%
0.99 10694
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 103063
27.4%
. 103063
27.4%
9 78057
20.7%
5 45765
12.2%
1 35700
 
9.5%
8 10904
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 273489
72.6%
Other Punctuation 103063
 
27.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103063
37.7%
9 78057
28.5%
5 45765
16.7%
1 35700
 
13.1%
8 10904
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 103063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 376552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103063
27.4%
. 103063
27.4%
9 78057
20.7%
5 45765
12.2%
1 35700
 
9.5%
8 10904
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103063
27.4%
. 103063
27.4%
9 78057
20.7%
5 45765
12.2%
1 35700
 
9.5%
8 10904
 
2.9%

elec_cons
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3972.9037
Minimum1631
Maximum7413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:55.271357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1631
5-th percentile1631
Q12060
median4076
Q34343
95-th percentile7413
Maximum7413
Range5782
Interquartile range (IQR)2283

Descriptive statistics

Standard deviation1827.8918
Coefficient of variation (CV)0.46008962
Kurtosis-0.37486132
Mean3972.9037
Median Absolute Deviation (MAD)267
Skewness0.61543089
Sum4.0945938 × 108
Variance3341188.3
MonotonicityNot monotonic
2023-08-04T22:06:55.339060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4076 27431
26.6%
7413 16917
16.4%
4343 15978
15.5%
1631 12870
12.5%
2060 10904
 
10.6%
3984 10694
 
10.4%
2031 8269
 
8.0%
ValueCountFrequency (%)
1631 12870
12.5%
2031 8269
 
8.0%
2060 10904
 
10.6%
3984 10694
 
10.4%
4076 27431
26.6%
4343 15978
15.5%
7413 16917
16.4%
ValueCountFrequency (%)
7413 16917
16.4%
4343 15978
15.5%
4076 27431
26.6%
3984 10694
 
10.4%
2060 10904
 
10.6%
2031 8269
 
8.0%
1631 12870
12.5%

building_perm
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1660.7373
Minimum583
Maximum2959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:55.418189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum583
5-th percentile583
Q1810
median1528
Q32959
95-th percentile2959
Maximum2959
Range2376
Interquartile range (IQR)2149

Descriptive statistics

Standard deviation881.48795
Coefficient of variation (CV)0.53078109
Kurtosis-1.2468568
Mean1660.7373
Median Absolute Deviation (MAD)718
Skewness0.40097098
Sum1.7116057 × 108
Variance777021
MonotonicityNot monotonic
2023-08-04T22:06:55.499825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2959 27431
26.6%
583 16917
16.4%
1528 15978
15.5%
978 11980
11.6%
810 10904
 
10.6%
1763 10694
 
10.4%
1829 8269
 
8.0%
1336 890
 
0.9%
ValueCountFrequency (%)
583 16917
16.4%
810 10904
 
10.6%
978 11980
11.6%
1336 890
 
0.9%
1528 15978
15.5%
1763 10694
 
10.4%
1829 8269
 
8.0%
2959 27431
26.6%
ValueCountFrequency (%)
2959 27431
26.6%
1829 8269
 
8.0%
1763 10694
 
10.4%
1528 15978
15.5%
1336 890
 
0.9%
978 11980
11.6%
810 10904
 
10.6%
583 16917
16.4%

land_permited
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1866989.3
Minimum695718
Maximum3019546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:55.579140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum695718
5-th percentile695718
Q1782505
median2133640
Q33019546
95-th percentile3019546
Maximum3019546
Range2323828
Interquartile range (IQR)2237041

Descriptive statistics

Standard deviation879476.15
Coefficient of variation (CV)0.47106653
Kurtosis-1.4565754
Mean1866989.3
Median Absolute Deviation (MAD)885906
Skewness0.0010874755
Sum1.9241752 × 1011
Variance7.734783 × 1011
MonotonicityNot monotonic
2023-08-04T22:06:55.658554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3019546 27431
26.6%
782505 16917
16.4%
2222311 15978
15.5%
1189247 11980
11.6%
695718 10904
 
10.6%
2133640 10694
 
10.4%
1743251 8269
 
8.0%
1996910 890
 
0.9%
ValueCountFrequency (%)
695718 10904
 
10.6%
782505 16917
16.4%
1189247 11980
11.6%
1743251 8269
 
8.0%
1996910 890
 
0.9%
2133640 10694
 
10.4%
2222311 15978
15.5%
3019546 27431
26.6%
ValueCountFrequency (%)
3019546 27431
26.6%
2222311 15978
15.5%
2133640 10694
 
10.4%
1996910 890
 
0.9%
1743251 8269
 
8.0%
1189247 11980
11.6%
782505 16917
16.4%
695718 10904
 
10.6%

labour_fource
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
48.1
43589 
50.0
38335 
40.6
12870 
48.3
8269 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters412252
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50.0
2nd row50.0
3rd row50.0
4th row50.0
5th row50.0

Common Values

ValueCountFrequency (%)
48.1 43589
42.3%
50.0 38335
37.2%
40.6 12870
 
12.5%
48.3 8269
 
8.0%

Length

2023-08-04T22:06:55.749201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:55.840985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
48.1 43589
42.3%
50.0 38335
37.2%
40.6 12870
 
12.5%
48.3 8269
 
8.0%

Most occurring characters

ValueCountFrequency (%)
. 103063
25.0%
0 89540
21.7%
4 64728
15.7%
8 51858
12.6%
1 43589
10.6%
5 38335
 
9.3%
6 12870
 
3.1%
3 8269
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 309189
75.0%
Other Punctuation 103063
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89540
29.0%
4 64728
20.9%
8 51858
16.8%
1 43589
14.1%
5 38335
12.4%
6 12870
 
4.2%
3 8269
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 103063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 412252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 103063
25.0%
0 89540
21.7%
4 64728
15.7%
8 51858
12.6%
1 43589
10.6%
5 38335
 
9.3%
6 12870
 
3.1%
3 8269
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 412252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 103063
25.0%
0 89540
21.7%
4 64728
15.7%
8 51858
12.6%
1 43589
10.6%
5 38335
 
9.3%
6 12870
 
3.1%
3 8269
 
2.0%

unemployment
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
17.1
43589 
10.1
38335 
15.0
12870 
10.2
8269 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters412252
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.1
2nd row10.1
3rd row10.1
4th row10.1
5th row10.1

Common Values

ValueCountFrequency (%)
17.1 43589
42.3%
10.1 38335
37.2%
15.0 12870
 
12.5%
10.2 8269
 
8.0%

Length

2023-08-04T22:06:55.925824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T22:06:56.021090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
17.1 43589
42.3%
10.1 38335
37.2%
15.0 12870
 
12.5%
10.2 8269
 
8.0%

Most occurring characters

ValueCountFrequency (%)
1 184987
44.9%
. 103063
25.0%
0 59474
 
14.4%
7 43589
 
10.6%
5 12870
 
3.1%
2 8269
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 309189
75.0%
Other Punctuation 103063
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 184987
59.8%
0 59474
 
19.2%
7 43589
 
14.1%
5 12870
 
4.2%
2 8269
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 103063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 412252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 184987
44.9%
. 103063
25.0%
0 59474
 
14.4%
7 43589
 
10.6%
5 12870
 
3.1%
2 8269
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 412252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 184987
44.9%
. 103063
25.0%
0 59474
 
14.4%
7 43589
 
10.6%
5 12870
 
3.1%
2 8269
 
2.0%

agricultural
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40083567
Minimum1233061
Maximum3.5350852 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:56.103075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1233061
5-th percentile1233061
Q12336012
median3466185
Q33535085
95-th percentile3.5350852 × 108
Maximum3.5350852 × 108
Range3.5227546 × 108
Interquartile range (IQR)1199073

Descriptive statistics

Standard deviation1.066765 × 108
Coefficient of variation (CV)2.6613524
Kurtosis4.7448938
Mean40083567
Median Absolute Deviation (MAD)736237
Skewness2.5959069
Sum4.1311327 × 1012
Variance1.1379875 × 1016
MonotonicityNot monotonic
2023-08-04T22:06:56.182232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3466185 27431
26.6%
1233061 16917
16.4%
3535085 15978
15.5%
10445551 11980
11.6%
2336012 10904
 
10.6%
353508523 10694
 
10.4%
2729948 8269
 
8.0%
5735761 890
 
0.9%
ValueCountFrequency (%)
1233061 16917
16.4%
2336012 10904
 
10.6%
2729948 8269
 
8.0%
3466185 27431
26.6%
3535085 15978
15.5%
5735761 890
 
0.9%
10445551 11980
11.6%
353508523 10694
 
10.4%
ValueCountFrequency (%)
353508523 10694
 
10.4%
10445551 11980
11.6%
5735761 890
 
0.9%
3535085 15978
15.5%
3466185 27431
26.6%
2729948 8269
 
8.0%
2336012 10904
 
10.6%
1233061 16917
16.4%

life_time
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.11588
Minimum76.9
Maximum79.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:56.261986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum76.9
5-th percentile76.9
Q176.9
median78
Q379.2
95-th percentile79.7
Maximum79.7
Range2.8
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation0.96765875
Coefficient of variation (CV)0.012387478
Kurtosis-1.2684846
Mean78.11588
Median Absolute Deviation (MAD)1.1
Skewness0.20931107
Sum8050856.9
Variance0.93636347
MonotonicityNot monotonic
2023-08-04T22:06:56.330804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
76.9 27431
26.6%
78 16917
16.4%
79.2 15978
15.5%
77.7 11980
11.6%
79.7 10904
 
10.6%
77.9 10694
 
10.4%
79 8269
 
8.0%
78.9 890
 
0.9%
ValueCountFrequency (%)
76.9 27431
26.6%
77.7 11980
11.6%
77.9 10694
 
10.4%
78 16917
16.4%
78.9 890
 
0.9%
79 8269
 
8.0%
79.2 15978
15.5%
79.7 10904
 
10.6%
ValueCountFrequency (%)
79.7 10904
 
10.6%
79.2 15978
15.5%
79 8269
 
8.0%
78.9 890
 
0.9%
78 16917
16.4%
77.9 10694
 
10.4%
77.7 11980
11.6%
76.9 27431
26.6%

hb_per100000
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.96233
Minimum193
Maximum369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:56.407199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum193
5-th percentile193
Q1246
median256
Q3306
95-th percentile369
Maximum369
Range176
Interquartile range (IQR)60

Descriptive statistics

Standard deviation46.708026
Coefficient of variation (CV)0.17496111
Kurtosis-0.21382887
Mean266.96233
Median Absolute Deviation (MAD)34
Skewness0.41912405
Sum27513939
Variance2181.6397
MonotonicityNot monotonic
2023-08-04T22:06:56.482032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
306 27431
26.6%
246 16917
16.4%
256 15978
15.5%
193 11980
11.6%
222 10904
 
10.6%
266 10694
 
10.4%
369 8269
 
8.0%
269 890
 
0.9%
ValueCountFrequency (%)
193 11980
11.6%
222 10904
 
10.6%
246 16917
16.4%
256 15978
15.5%
266 10694
 
10.4%
269 890
 
0.9%
306 27431
26.6%
369 8269
 
8.0%
ValueCountFrequency (%)
369 8269
 
8.0%
306 27431
26.6%
269 890
 
0.9%
266 10694
 
10.4%
256 15978
15.5%
246 16917
16.4%
222 10904
 
10.6%
193 11980
11.6%

fertility
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3764133
Minimum1.63
Maximum3.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:56.562601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.63
5-th percentile1.63
Q11.99
median2.36
Q32.47
95-th percentile3.81
Maximum3.81
Range2.18
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.58621242
Coefficient of variation (CV)0.24667949
Kurtosis1.4583902
Mean2.3764133
Median Absolute Deviation (MAD)0.37
Skewness1.4138797
Sum244920.28
Variance0.343645
MonotonicityNot monotonic
2023-08-04T22:06:56.630007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.47 27431
26.6%
1.92 16917
16.4%
1.99 15978
15.5%
3.81 11980
11.6%
2.36 10904
 
10.6%
2.41 10694
 
10.4%
1.63 8269
 
8.0%
2.54 890
 
0.9%
ValueCountFrequency (%)
1.63 8269
 
8.0%
1.92 16917
16.4%
1.99 15978
15.5%
2.36 10904
 
10.6%
2.41 10694
 
10.4%
2.47 27431
26.6%
2.54 890
 
0.9%
3.81 11980
11.6%
ValueCountFrequency (%)
3.81 11980
11.6%
2.54 890
 
0.9%
2.47 27431
26.6%
2.41 10694
 
10.4%
2.36 10904
 
10.6%
1.99 15978
15.5%
1.92 16917
16.4%
1.63 8269
 
8.0%

hh_size
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9026291
Minimum3.4
Maximum5.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:56.701991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile3.4
Q13.65
median3.68
Q33.97
95-th percentile5.12
Maximum5.12
Range1.72
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.49353631
Coefficient of variation (CV)0.12646252
Kurtosis1.5207558
Mean3.9026291
Median Absolute Deviation (MAD)0.28
Skewness1.5252223
Sum402216.66
Variance0.24357809
MonotonicityNot monotonic
2023-08-04T22:06:56.775261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3.97 26570
25.8%
3.46 16917
16.4%
3.68 15978
15.5%
5.12 11980
11.6%
4 10904
10.6%
3.65 10694
10.4%
3.4 8269
 
8.0%
4.43 890
 
0.9%
3.9 861
 
0.8%
ValueCountFrequency (%)
3.4 8269
 
8.0%
3.46 16917
16.4%
3.65 10694
10.4%
3.68 15978
15.5%
3.9 861
 
0.8%
3.97 26570
25.8%
4 10904
10.6%
4.43 890
 
0.9%
5.12 11980
11.6%
ValueCountFrequency (%)
5.12 11980
11.6%
4.43 890
 
0.9%
4 10904
10.6%
3.97 26570
25.8%
3.9 861
 
0.8%
3.68 15978
15.5%
3.65 10694
10.4%
3.46 16917
16.4%
3.4 8269
 
8.0%

longitude
Real number (ℝ)

Distinct98600
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.357219
Minimum36.114272
Maximum40.254908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:56.875895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36.114272
5-th percentile36.161382
Q136.574224
median37.334245
Q338.258965
95-th percentile38.80236
Maximum40.254908
Range4.1406361
Interquartile range (IQR)1.6847407

Descriptive statistics

Standard deviation0.8896378
Coefficient of variation (CV)0.023814348
Kurtosis-0.58018
Mean37.357219
Median Absolute Deviation (MAD)0.88125276
Skewness0.49389691
Sum3850147.1
Variance0.79145542
MonotonicityNot monotonic
2023-08-04T22:06:56.983089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.61950921 3
 
< 0.1%
36.64408218 3
 
< 0.1%
36.63091653 3
 
< 0.1%
36.64243944 3
 
< 0.1%
36.63740374 3
 
< 0.1%
36.63930318 3
 
< 0.1%
36.64167509 3
 
< 0.1%
36.64549728 3
 
< 0.1%
36.64089119 3
 
< 0.1%
36.62723019 3
 
< 0.1%
Other values (98590) 103033
> 99.9%
ValueCountFrequency (%)
36.11427197 1
< 0.1%
36.11439755 1
< 0.1%
36.11443127 1
< 0.1%
36.11443137 1
< 0.1%
36.11450452 1
< 0.1%
36.11492625 1
< 0.1%
36.11497942 1
< 0.1%
36.1151249 1
< 0.1%
36.11518601 1
< 0.1%
36.1152937 1
< 0.1%
ValueCountFrequency (%)
40.25490806 1
< 0.1%
40.25379994 1
< 0.1%
40.24945253 1
< 0.1%
40.24800833 1
< 0.1%
40.24797433 1
< 0.1%
40.24777372 1
< 0.1%
40.247749 1
< 0.1%
40.24569095 1
< 0.1%
40.24541895 1
< 0.1%
40.24440365 1
< 0.1%

latitude
Real number (ℝ)

Distinct98567
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.290424
Minimum36.128308
Maximum38.396723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:57.095226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36.128308
5-th percentile36.220374
Q137.063384
median37.174594
Q337.591139
95-th percentile38.340081
Maximum38.396723
Range2.2684145
Interquartile range (IQR)0.52775463

Descriptive statistics

Standard deviation0.52344437
Coefficient of variation (CV)0.014036965
Kurtosis0.2436901
Mean37.290424
Median Absolute Deviation (MAD)0.14328
Skewness0.085439145
Sum3843263
Variance0.27399401
MonotonicityNot monotonic
2023-08-04T22:06:57.209467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.0975489 4
 
< 0.1%
37.00114387 3
 
< 0.1%
37.02339442 3
 
< 0.1%
37.0236209 3
 
< 0.1%
37.02323304 3
 
< 0.1%
37.02416652 3
 
< 0.1%
37.02367243 3
 
< 0.1%
37.02356448 3
 
< 0.1%
37.02378129 3
 
< 0.1%
37.02339537 3
 
< 0.1%
Other values (98557) 103032
> 99.9%
ValueCountFrequency (%)
36.1283083 1
< 0.1%
36.12837045 1
< 0.1%
36.1284151 1
< 0.1%
36.12868412 1
< 0.1%
36.12870294 1
< 0.1%
36.12882003 1
< 0.1%
36.12892498 1
< 0.1%
36.12896239 1
< 0.1%
36.12911726 1
< 0.1%
36.1291561 1
< 0.1%
ValueCountFrequency (%)
38.39672285 1
< 0.1%
38.39668075 1
< 0.1%
38.39659454 1
< 0.1%
38.39635137 1
< 0.1%
38.39595051 1
< 0.1%
38.39578059 1
< 0.1%
38.39571403 1
< 0.1%
38.39566391 1
< 0.1%
38.39562311 1
< 0.1%
38.39557557 1
< 0.1%
Distinct98670
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023548382
Minimum2.0670027 × 10-6
Maximum0.37140012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:57.330902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.0670027 × 10-6
5-th percentile0.0023632666
Q10.0069561684
median0.013402879
Q30.025906465
95-th percentile0.058205952
Maximum0.37140012
Range0.37139805
Interquartile range (IQR)0.018950296

Descriptive statistics

Standard deviation0.039658547
Coefficient of variation (CV)1.6841304
Kurtosis34.717925
Mean0.023548382
Median Absolute Deviation (MAD)0.0080607036
Skewness5.5003996
Sum2426.9669
Variance0.0015728003
MonotonicityNot monotonic
2023-08-04T22:06:57.442241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01990762516 3
 
< 0.1%
0.02453883495 3
 
< 0.1%
0.02035356102 3
 
< 0.1%
0.01703777669 3
 
< 0.1%
0.02841953374 3
 
< 0.1%
0.02938895287 3
 
< 0.1%
0.02533618225 3
 
< 0.1%
0.02444101268 3
 
< 0.1%
0.01733062133 3
 
< 0.1%
0.01663910276 3
 
< 0.1%
Other values (98660) 103033
> 99.9%
ValueCountFrequency (%)
2.067002653 × 10-61
< 0.1%
5.165510609 × 10-61
< 0.1%
9.994743099 × 10-61
< 0.1%
1.088839431 × 10-51
< 0.1%
1.209338661 × 10-51
< 0.1%
1.801256506 × 10-51
< 0.1%
2.560603112 × 10-51
< 0.1%
3.102125537 × 10-51
< 0.1%
3.969922945 × 10-51
< 0.1%
4.034398488 × 10-51
< 0.1%
ValueCountFrequency (%)
0.3714001198 1
< 0.1%
0.3713324109 1
< 0.1%
0.3710841901 1
< 0.1%
0.3708692335 1
< 0.1%
0.3706587069 1
< 0.1%
0.3705728955 1
< 0.1%
0.3705412716 1
< 0.1%
0.3704414072 1
< 0.1%
0.3703046694 1
< 0.1%
0.370265392 1
< 0.1%

nearest_camping_distance
Real number (ℝ)

Distinct98670
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10005087
Minimum5.7490217 × 10-7
Maximum0.84134724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:57.570041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.7490217 × 10-7
5-th percentile0.0012199807
Q10.0038298683
median0.0075875869
Q30.016519893
95-th percentile0.78128512
Maximum0.84134724
Range0.84134667
Interquartile range (IQR)0.012690025

Descriptive statistics

Standard deviation0.24670718
Coefficient of variation (CV)2.4658175
Kurtosis3.6688412
Mean0.10005087
Median Absolute Deviation (MAD)0.0047028102
Skewness2.3693788
Sum10311.543
Variance0.060864432
MonotonicityNot monotonic
2023-08-04T22:06:57.682850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.007668441018 3
 
< 0.1%
0.003090826996 3
 
< 0.1%
0.003020488577 3
 
< 0.1%
0.001060775936 3
 
< 0.1%
0.004401030971 3
 
< 0.1%
0.005392544282 3
 
< 0.1%
0.003478524356 3
 
< 0.1%
0.002360800349 3
 
< 0.1%
0.00172604272 3
 
< 0.1%
0.002706987187 3
 
< 0.1%
Other values (98660) 103033
> 99.9%
ValueCountFrequency (%)
5.749021662 × 10-71
< 0.1%
3.053957863 × 10-61
< 0.1%
3.102738931 × 10-61
< 0.1%
3.43447596 × 10-61
< 0.1%
3.701064828 × 10-61
< 0.1%
5.353677044 × 10-61
< 0.1%
6.664345637 × 10-61
< 0.1%
7.421216988 × 10-61
< 0.1%
8.191871646 × 10-61
< 0.1%
9.400498266 × 10-61
< 0.1%
ValueCountFrequency (%)
0.8413472442 1
< 0.1%
0.8412976673 1
< 0.1%
0.8411405664 1
< 0.1%
0.8407918353 1
< 0.1%
0.8403436621 1
< 0.1%
0.8402239012 1
< 0.1%
0.8397051804 1
< 0.1%
0.8395599064 1
< 0.1%
0.8393391952 1
< 0.1%
0.8392080263 1
< 0.1%
Distinct98670
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045467312
Minimum4.6714486 × 10-5
Maximum0.24142475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:57.791394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.6714486 × 10-5
5-th percentile0.0036728384
Q10.010733885
median0.024415215
Q30.07410546
95-th percentile0.14622369
Maximum0.24142475
Range0.24137804
Interquartile range (IQR)0.063371575

Descriptive statistics

Standard deviation0.044991336
Coefficient of variation (CV)0.98953146
Kurtosis0.70773472
Mean0.045467312
Median Absolute Deviation (MAD)0.018586935
Skewness1.2239179
Sum4685.9976
Variance0.0020242203
MonotonicityNot monotonic
2023-08-04T22:06:57.909865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005770506981 3
 
< 0.1%
0.01082112331 3
 
< 0.1%
0.008272967507 3
 
< 0.1%
0.004990534142 3
 
< 0.1%
0.006549291648 3
 
< 0.1%
0.00632002512 3
 
< 0.1%
0.01052257448 3
 
< 0.1%
0.01163174964 3
 
< 0.1%
0.005465678145 3
 
< 0.1%
0.006659895552 3
 
< 0.1%
Other values (98660) 103033
> 99.9%
ValueCountFrequency (%)
4.671448626 × 10-51
< 0.1%
6.310514561 × 10-51
< 0.1%
7.208064934 × 10-51
< 0.1%
7.287247023 × 10-51
< 0.1%
8.074750679 × 10-51
< 0.1%
0.0001109429617 1
< 0.1%
0.0001145850403 1
< 0.1%
0.000121604252 1
< 0.1%
0.0001216689207 1
< 0.1%
0.0001335900538 1
< 0.1%
ValueCountFrequency (%)
0.2414247528 1
< 0.1%
0.2411643059 1
< 0.1%
0.2400758451 1
< 0.1%
0.2339184871 1
< 0.1%
0.231997316 1
< 0.1%
0.2319599862 1
< 0.1%
0.2308040546 1
< 0.1%
0.2307150981 1
< 0.1%
0.2301813049 1
< 0.1%
0.228303161 1
< 0.1%

nearest_fault_distance
Real number (ℝ)

Distinct98670
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17032685
Minimum0.00016756777
Maximum0.34059152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:58.028378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00016756777
5-th percentile0.02129101
Q10.064884427
median0.19017933
Q30.25731124
95-th percentile0.29228729
Maximum0.34059152
Range0.34042395
Interquartile range (IQR)0.19242682

Descriptive statistics

Standard deviation0.095645126
Coefficient of variation (CV)0.56153876
Kurtosis-1.4089625
Mean0.17032685
Median Absolute Deviation (MAD)0.079430669
Skewness-0.29607718
Sum17554.396
Variance0.0091479902
MonotonicityNot monotonic
2023-08-04T22:06:58.136603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01513961758 3
 
< 0.1%
0.01699088882 3
 
< 0.1%
0.01190986508 3
 
< 0.1%
0.007726599179 3
 
< 0.1%
0.02136946969 3
 
< 0.1%
0.02224687054 3
 
< 0.1%
0.01770298736 3
 
< 0.1%
0.01665088954 3
 
< 0.1%
0.007980871717 3
 
< 0.1%
0.007559189044 3
 
< 0.1%
Other values (98660) 103033
> 99.9%
ValueCountFrequency (%)
0.0001675677727 1
 
< 0.1%
0.0001732226849 1
 
< 0.1%
0.001013617125 3
< 0.1%
0.001113509456 1
 
< 0.1%
0.001233883744 1
 
< 0.1%
0.001265949178 1
 
< 0.1%
0.001343041603 1
 
< 0.1%
0.001376093758 1
 
< 0.1%
0.001518182418 1
 
< 0.1%
0.001557743585 3
< 0.1%
ValueCountFrequency (%)
0.3405915221 1
< 0.1%
0.3390441569 1
< 0.1%
0.3388776962 1
< 0.1%
0.3387301679 1
< 0.1%
0.3386681656 1
< 0.1%
0.338498991 1
< 0.1%
0.3383105793 1
< 0.1%
0.3380994351 1
< 0.1%
0.3380936061 1
< 0.1%
0.3380388732 1
< 0.1%

elev
Real number (ℝ)

Distinct122
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean626.53566
Minimum70
Maximum1270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.3 KiB
2023-08-04T22:06:58.500920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile110
Q1500
median660
Q3860
95-th percentile980
Maximum1270
Range1200
Interquartile range (IQR)360

Descriptive statistics

Standard deviation280.53236
Coefficient of variation (CV)0.44775163
Kurtosis-0.66397213
Mean626.53566
Median Absolute Deviation (MAD)200
Skewness-0.44228649
Sum64572645
Variance78698.407
MonotonicityNot monotonic
2023-08-04T22:06:58.608705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
860 6019
 
5.8%
690 5833
 
5.7%
880 4429
 
4.3%
660 4037
 
3.9%
510 4030
 
3.9%
840 4007
 
3.9%
900 3826
 
3.7%
540 3821
 
3.7%
570 3138
 
3.0%
940 2977
 
2.9%
Other values (112) 60946
59.1%
ValueCountFrequency (%)
70 42
 
< 0.1%
80 466
 
0.5%
90 1957
1.9%
100 2090
2.0%
110 2489
2.4%
120 2339
2.3%
130 1998
1.9%
140 1178
1.1%
150 923
 
0.9%
160 650
 
0.6%
ValueCountFrequency (%)
1270 3
 
< 0.1%
1260 5
 
< 0.1%
1250 3
 
< 0.1%
1240 10
 
< 0.1%
1230 19
< 0.1%
1220 31
< 0.1%
1210 20
< 0.1%
1200 23
< 0.1%
1190 43
< 0.1%
1180 41
< 0.1%

geometry
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct98670
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size805.3 KiB
POINT (36.6268 37.0145)
 
3
POINT (36.626 37.0166)
 
3
POINT (36.6353 37.0241)
 
3
POINT (36.641 37.0299)
 
3
POINT (36.6278 37.0249)
 
3
Other values (98665)
103048 

Length

Max length23
Median length23
Mean length22.779921
Min length17

Characters and Unicode

Total characters2347767
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94806 ?
Unique (%)92.0%

Sample

1st rowPOINT (38.3143 37.7689)
2nd rowPOINT (38.3133 37.7687)
3rd rowPOINT (38.318 37.7686)
4th rowPOINT (38.3187 37.7686)
5th rowPOINT (38.3125 37.7685)

Common Values

ValueCountFrequency (%)
POINT (36.6268 37.0145) 3
 
< 0.1%
POINT (36.626 37.0166) 3
 
< 0.1%
POINT (36.6353 37.0241) 3
 
< 0.1%
POINT (36.641 37.0299) 3
 
< 0.1%
POINT (36.6278 37.0249) 3
 
< 0.1%
POINT (36.6379 37.0296) 3
 
< 0.1%
POINT (36.6252 37.0153) 3
 
< 0.1%
POINT (36.6313 37.0155) 3
 
< 0.1%
POINT (36.6304 37.0161) 3
 
< 0.1%
POINT (36.6245 37.0162) 3
 
< 0.1%
Other values (98660) 103033
> 99.9%

Length

2023-08-04T22:06:58.710349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
point 103063
33.3%
37.2402 69
 
< 0.1%
37.074 69
 
< 0.1%
37.0691 68
 
< 0.1%
37.5829 68
 
< 0.1%
37.0825 67
 
< 0.1%
37.0743 67
 
< 0.1%
37.5846 66
 
< 0.1%
37.0861 65
 
< 0.1%
37.0732 65
 
< 0.1%
Other values (16771) 205522
66.5%

Most occurring characters

ValueCountFrequency (%)
3 301766
 
12.9%
206126
 
8.8%
. 206125
 
8.8%
7 191609
 
8.2%
6 125521
 
5.3%
8 115271
 
4.9%
) 103063
 
4.4%
O 103063
 
4.4%
P 103063
 
4.4%
( 103063
 
4.4%
Other values (9) 789097
33.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1214075
51.7%
Uppercase Letter 515315
21.9%
Space Separator 206126
 
8.8%
Other Punctuation 206125
 
8.8%
Close Punctuation 103063
 
4.4%
Open Punctuation 103063
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 301766
24.9%
7 191609
15.8%
6 125521
10.3%
8 115271
 
9.5%
2 97648
 
8.0%
4 84776
 
7.0%
5 82299
 
6.8%
1 80296
 
6.6%
9 69957
 
5.8%
0 64932
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 103063
20.0%
P 103063
20.0%
T 103063
20.0%
N 103063
20.0%
I 103063
20.0%
Space Separator
ValueCountFrequency (%)
206126
100.0%
Other Punctuation
ValueCountFrequency (%)
. 206125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 103063
100.0%
Open Punctuation
ValueCountFrequency (%)
( 103063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1832452
78.1%
Latin 515315
 
21.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 301766
16.5%
206126
11.2%
. 206125
11.2%
7 191609
10.5%
6 125521
6.8%
8 115271
 
6.3%
) 103063
 
5.6%
( 103063
 
5.6%
2 97648
 
5.3%
4 84776
 
4.6%
Other values (4) 297484
16.2%
Latin
ValueCountFrequency (%)
O 103063
20.0%
P 103063
20.0%
T 103063
20.0%
N 103063
20.0%
I 103063
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2347767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 301766
 
12.9%
206126
 
8.8%
. 206125
 
8.8%
7 191609
 
8.2%
6 125521
 
5.3%
8 115271
 
4.9%
) 103063
 
4.4%
O 103063
 
4.4%
P 103063
 
4.4%
( 103063
 
4.4%
Other values (9) 789097
33.6%

Interactions

2023-08-04T22:06:46.841118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:05:57.066742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:05:59.304912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-08-04T22:06:07.417591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:09.711063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:12.414965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:14.793821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:17.150009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:19.758876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:22.380539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:24.653724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:27.129820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:29.367089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:32.024436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:34.438262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:36.771314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:39.340187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:41.992537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:44.308494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:46.616412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:49.340408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:05:59.146731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:03.040510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:05.298712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:07.529638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:09.833222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:12.534924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:14.917741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:17.305001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:19.876311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:22.499603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:24.765086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:27.276803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:29.464639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:32.146075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:34.565070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:36.883741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:39.470934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:42.096632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:44.416033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-08-04T22:06:46.722167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-08-04T22:06:58.845087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
or_src_idarea_idpopulationincometotal_salessecond_saleselec_consbuilding_permland_permitedagriculturallife_timehb_per100000fertilityhh_sizelongitudelatitudenearest_water_source_distancenearest_camping_distancenearest_earthquake_distancenearest_fault_distanceelevobj_typeinfodamage_gradet_methodnotationdmg_src_idrealglide_nolocalitymap_typewater_accesslabour_fourceunemployment
or_src_id1.000-0.4050.0550.4670.2320.2590.2210.5030.5410.093-0.1770.5290.023-0.108-0.254-0.2870.199-0.315-0.1370.0560.2060.6760.5270.1510.0530.5120.3790.4440.3020.7540.3820.4450.4630.463
area_id-0.4051.000-0.576-0.326-0.802-0.7560.110-0.535-0.4660.0930.246-0.384-0.315-0.404-0.4130.036-0.4090.064-0.487-0.480-0.5770.2570.1550.4680.8630.5840.6940.9961.0001.0001.0001.0001.0001.000
population0.055-0.5761.0000.0620.9300.939-0.5340.4880.4200.455-0.5120.1580.6520.5290.639-0.1610.1970.3850.6420.7790.3720.2790.2500.1280.1220.3720.1850.8520.5681.0000.3410.4940.5760.576
income0.467-0.3260.0621.0000.2750.3170.2030.7510.645-0.174-0.5500.922-0.071-0.417-0.256-0.4010.007-0.295-0.2680.2880.3690.2360.2150.1200.0960.3510.3490.5130.4231.0000.6981.0000.8300.830
total_sales0.232-0.8020.9300.2751.0000.992-0.3920.6360.5560.273-0.5200.3600.5810.4900.598-0.1520.2860.2230.6090.8040.5720.3170.2340.1270.1190.4560.3750.8520.5561.0000.7490.6210.7810.781
second_sales0.259-0.7560.9390.3170.9921.000-0.3790.6710.6000.340-0.5700.4000.5890.4370.529-0.2270.2510.2190.5640.8040.5250.2910.2180.1280.1190.4900.4140.8520.5561.0000.9120.6840.9210.921
elec_cons0.2210.110-0.5340.203-0.392-0.3791.000-0.1900.141-0.4140.0640.067-0.475-0.511-0.722-0.102-0.167-0.326-0.392-0.205-0.1900.3160.1950.0850.0560.3920.2810.4940.6541.0000.6380.5350.6360.636
building_perm0.503-0.5350.4880.7510.6360.671-0.1901.0000.8990.365-0.6130.8460.4120.1070.115-0.3890.176-0.1230.0300.4760.5170.2950.1630.1360.1390.3450.4550.9120.4771.0000.9140.8650.9290.929
land_permited0.541-0.4660.4200.6450.5560.6000.1410.8991.0000.414-0.6280.7330.4070.084-0.086-0.4500.123-0.1380.0040.4600.3830.2930.2330.1160.1030.3210.4340.9120.4771.0000.9090.7090.9440.944
agricultural0.0930.0930.455-0.1740.2730.340-0.4140.3650.4141.000-0.2420.0160.5710.3280.045-0.355-0.0160.1570.1020.116-0.1830.1280.1000.2060.0220.1130.2980.1210.0991.0000.0001.0000.3970.397
life_time-0.1770.246-0.512-0.550-0.520-0.5700.064-0.613-0.628-0.2421.000-0.393-0.677-0.247-0.0130.765-0.110-0.096-0.328-0.469-0.0670.2920.1640.1000.1440.3850.4110.9120.4791.0000.8790.8570.9730.973
hb_per1000000.529-0.3840.1580.9220.3600.4000.0670.8460.7330.016-0.3931.000-0.082-0.381-0.118-0.2090.020-0.268-0.2980.3550.5040.2720.1290.1190.0960.3940.4440.9120.4231.0000.9110.9840.9860.986
fertility0.023-0.3150.652-0.0710.5810.589-0.4750.4120.4070.571-0.677-0.0821.0000.8370.391-0.5280.3130.2500.6500.3130.0320.2010.2310.0660.0960.3100.3690.2920.4231.0000.8990.6060.9290.929
hh_size-0.108-0.4040.529-0.4170.4900.437-0.5110.1070.0840.328-0.247-0.3810.8371.0000.625-0.0920.4130.2800.7440.1900.1530.2340.2230.1210.1030.2470.4760.4120.5101.0000.7240.5940.8570.857
longitude-0.254-0.4130.639-0.2560.5980.529-0.7220.115-0.0860.045-0.013-0.1180.3910.6251.0000.4430.2890.3810.6000.5440.5560.2450.1920.1170.1480.4020.3490.8520.5270.9230.7570.6900.8270.827
latitude-0.2870.036-0.161-0.401-0.152-0.227-0.102-0.389-0.450-0.3550.765-0.209-0.528-0.0920.4431.000-0.0620.140-0.1050.0010.3770.1330.1480.2560.3060.3420.3370.9550.2950.8980.6460.8480.6700.670
nearest_water_source_distance0.199-0.4090.1970.0070.2860.251-0.1670.1760.123-0.016-0.1100.0200.3130.4130.289-0.0621.000-0.0150.3570.0680.2330.0730.1070.4080.8680.0910.3500.1290.1190.6720.2110.2650.2450.245
nearest_camping_distance-0.3150.0640.385-0.2950.2230.219-0.326-0.123-0.1380.157-0.096-0.2680.2500.2800.3810.140-0.0151.0000.3650.2790.0690.0900.1000.0500.0240.2040.2750.1660.1080.6330.6810.2420.5570.557
nearest_earthquake_distance-0.137-0.4870.642-0.2680.6090.564-0.3920.0300.0040.102-0.328-0.2980.6500.7440.600-0.1050.3570.3651.0000.4000.2140.1550.1200.0970.0500.1850.3180.3170.2830.4680.6060.4240.6940.694
nearest_fault_distance0.056-0.4800.7790.2880.8040.804-0.2050.4760.4600.116-0.4690.3550.3130.1900.5440.0010.0680.2790.4001.0000.5980.1790.1220.1190.1170.4140.1780.5140.8810.6490.4390.6060.5100.510
elev0.206-0.5770.3720.3690.5720.525-0.1900.5170.383-0.183-0.0670.5040.0320.1530.5560.3770.2330.0690.2140.5981.0000.2110.1120.1380.1700.3900.3190.4720.9600.7250.5160.7380.6630.663
obj_type0.6760.2570.2790.2360.3170.2910.3160.2950.2930.1280.2920.2720.2010.2340.2450.1330.0730.0900.1550.1790.2111.0000.9280.1280.0750.7810.2270.0000.2860.2570.2040.2930.2690.269
info0.5270.1550.2500.2150.2340.2180.1950.1630.2330.1000.1640.1290.2310.2230.1920.1480.1070.1000.1200.1220.1120.9281.0000.1210.0950.7050.1700.0550.2410.1550.1330.1170.1340.134
damage_gra0.1510.4680.1280.1200.1270.1280.0850.1360.1160.2060.1000.1190.0660.1210.1170.2560.4080.0500.0970.1190.1380.1280.1211.0000.8520.1540.3650.1790.0730.4680.0810.1280.0850.085
det_method0.0530.8630.1220.0960.1190.1190.0560.1390.1030.0220.1440.0960.0960.1030.1480.3060.8680.0240.0500.1170.1700.0750.0950.8521.0000.0620.5870.0000.0190.8630.0320.0730.0760.076
notation0.5120.5840.3720.3510.4560.4900.3920.3450.3210.1130.3850.3940.3100.2470.4020.3420.0910.2040.1850.4140.3900.7810.7050.1540.0621.0000.1171.0000.4140.5840.3000.2260.2670.267
dmg_src_id0.3790.6940.1850.3490.3750.4140.2810.4550.4340.2980.4110.4440.3690.4760.3490.3370.3500.2750.3180.1780.3190.2270.1700.3650.5870.1171.0000.3450.2360.6940.6990.2330.4010.401
real0.4440.9960.8520.5130.8520.8520.4940.9120.9120.1210.9120.9120.2920.4120.8520.9550.1290.1660.3170.5140.4720.0000.0550.1790.0001.0000.3451.0001.0000.9960.2390.5130.4000.400
glide_no0.3021.0000.5680.4230.5560.5560.6540.4770.4770.0990.4790.4230.4230.5100.5270.2950.1190.1080.2830.8810.9600.2860.2410.0730.0190.4140.2361.0001.0001.0000.1590.3240.3390.339
locality0.7541.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9230.8980.6720.6330.4680.6490.7250.2570.1550.4680.8630.5840.6940.9961.0001.0001.0001.0001.0001.000
map_type0.3821.0000.3410.6980.7490.9120.6380.9140.9090.0000.8790.9110.8990.7240.7570.6460.2110.6810.6060.4390.5160.2040.1330.0810.0320.3000.6990.2390.1591.0001.0000.1990.8780.878
water_access0.4451.0000.4941.0000.6210.6840.5350.8650.7091.0000.8570.9840.6060.5940.6900.8480.2650.2420.4240.6060.7380.2930.1170.1280.0730.2260.2330.5130.3241.0000.1991.0000.6150.615
labour_fource0.4631.0000.5760.8300.7810.9210.6360.9290.9440.3970.9730.9860.9290.8570.8270.6700.2450.5570.6940.5100.6630.2690.1340.0850.0760.2670.4010.4000.3391.0000.8780.6151.0001.000
unemployment0.4631.0000.5760.8300.7810.9210.6360.9290.9440.3970.9730.9860.9290.8570.8270.6700.2450.5570.6940.5100.6630.2690.1340.0850.0760.2670.4010.4000.3391.0000.8780.6151.0001.000

Missing values

2023-08-04T22:06:49.749823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-04T22:06:50.439412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-04T22:06:51.128299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

obj_typenameinfodamage_gradet_methodnotationor_src_iddmg_src_idcd_valuerealindex_rightemsr_idglide_noarea_idlocalitymap_typepopulationincometotal_salessecond_saleswater_accesselec_consbuilding_permland_permitedlabour_fourceunemploymentagriculturallife_timehb_per100000fertilityhh_sizelongitudelatitudenearest_water_source_distancenearest_camping_distancenearest_earthquake_distancenearest_fault_distanceelevgeometry
011-Residential BuildingsUnknown997-Not ApplicableNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31425237.7688670.0778740.0202320.0978450.016935660.0POINT (38.31425 37.76887)
111-Residential BuildingsUnknown997-Not ApplicableNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31328437.7686900.0769040.0192840.0970040.017440660.0POINT (38.31328 37.76869)
212-Non-residential BuildingsUnknown1251-Industrial buildingsNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31801337.7686120.0813360.0235880.1005270.014585660.0POINT (38.31801 37.76861)
312-Non-residential BuildingsUnknown1251-Industrial buildingsNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31867737.7685750.0819530.0241960.1010130.014241660.0POINT (38.31868 37.76857)
411-Residential BuildingsUnknown997-Not ApplicableNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31254237.7685140.0761460.0185420.0963340.017826660.0POINT (38.31254 37.76851)
511-Residential BuildingsUnknown997-Not ApplicableNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31442837.7683920.0778790.0201920.0976630.016458660.0POINT (38.31443 37.76839)
611-Residential BuildingsUnknown997-Not ApplicableNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31344837.7681860.0768860.0192150.0967910.016960660.0POINT (38.31345 37.76819)
712-Non-residential BuildingsUnknown1251-Industrial buildingsNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31796537.7677880.0810270.0232590.0999620.013896630.0POINT (38.31796 37.76779)
812-Non-residential BuildingsUnknown1251-Industrial buildingsNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.31863237.7677660.0816540.0238830.1004630.013546630.0POINT (38.31863 37.76777)
911-Residential BuildingsUnknown997-Not ApplicableNo visible damagePhoto-interpretationBuilding block13Not ApplicableNot Applicable0EMSR6482023-00001502ADIYAMANGrading-Monit01316140409240087205740.98206081069571850.010.1233601279.72222.364.038.30985637.7675120.0732790.0156960.0936750.019186660.0POINT (38.30986 37.76751)
obj_typenameinfodamage_gradet_methodnotationor_src_iddmg_src_idcd_valuerealindex_rightemsr_idglide_noarea_idlocalitymap_typepopulationincometotal_salessecond_saleswater_accesselec_consbuilding_permland_permitedlabour_fourceunemploymentagriculturallife_timehb_per100000fertilityhh_sizelongitudelatitudenearest_water_source_distancenearest_camping_distancenearest_earthquake_distancenearest_fault_distanceelevgeometry
103053212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.86238537.3756080.1897730.0131800.0028760.028053500.0POINT (36.86238 37.37561)
103054212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.86158837.3752970.1902680.0126780.0020510.028275500.0POINT (36.86159 37.37530)
103055212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.86162837.3751810.1903710.0127770.0020110.028395500.0POINT (36.86163 37.37518)
103056212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.86162337.3752880.1902680.0127120.0020720.028287500.0POINT (36.86162 37.37529)
103057212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.86186737.3753160.1901820.0128990.0022840.028284500.0POINT (36.86187 37.37532)
103058212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.85796537.3718510.1945120.0126110.0008520.031609500.0POINT (36.85797 37.37185)
103059212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.85817437.3715090.1947890.0130110.0005380.031945500.0POINT (36.85817 37.37151)
103060212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.85859337.3719810.1942260.0128570.0011460.031466500.0POINT (36.85859 37.37198)
103061212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.85778337.3720280.1943880.0123640.0010500.031438500.0POINT (36.85778 37.37203)
103062212-RailwaysNone997-Not ApplicableNo visible damageNot ApplicableNot Applicable9942Not ApplicableNone0EMSR6482023-00001517TurkogluGrading78976599719385360.9543431528222231148.117.1353508579.22561.993.6836.85765737.3722030.1942520.0121480.0010330.031268500.0POINT (36.85766 37.37220)

Duplicate rows

Most frequently occurring

obj_typenameinfodamage_gradet_methodnotationor_src_iddmg_src_idcd_valuerealindex_rightemsr_idglide_noarea_idlocalitymap_typepopulationincometotal_salessecond_saleswater_accesselec_consbuilding_permland_permitedlabour_fourceunemploymentagriculturallife_timehb_per100000fertilityhh_sizelongitudelatitudenearest_water_source_distancenearest_camping_distancenearest_earthquake_distancenearest_fault_distanceelevgeometry# duplicates
8811-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block33Not ApplicableNot Applicable0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.63402337.0152240.0250600.0033600.0109620.015697500.0POINT (36.63402 37.01522)3
8911-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.61498837.0018620.0031570.0226300.0122970.010608560.0POINT (36.61499 37.00186)3
9011-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.61597937.0021890.0041920.0218980.0113030.009882560.0POINT (36.61598 37.00219)3
9111-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.61631636.9965770.0046810.0269640.0150100.008268540.0POINT (36.61632 36.99658)3
9211-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.61901536.9950850.0035070.0276360.0141380.005959540.0POINT (36.61901 36.99508)3
9311-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.61921636.9935140.0024130.0291240.0152660.006532530.0POINT (36.61922 36.99351)3
9411-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.62028536.9933180.0032310.0291120.0148440.005824530.0POINT (36.62028 36.99332)3
9511-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.62071236.9942900.0040790.0280820.0137930.004868530.0POINT (36.62071 36.99429)3
9711-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.62171336.9968480.0065290.0254000.0111030.002874530.0POINT (36.62171 36.99685)3
9811-Residential BuildingsUnknown997-Not ApplicableDamagedPhoto-interpretationBuilding block9943Not ApplicableNone0EMSR6482023-00001510IslahiyeGrading-Monit01676507819449818921.040762959301954650.010.1346618576.93062.473.9736.62207536.9984240.0079790.0237890.0096180.002731540.0POINT (36.62208 36.99842)3